Factors Associated with Malnutrition in Pregnancy: A Principal

Component Analysis

Dian Isti Angraini

and Reni Zuraida

Department of Community Medicine and Public Health, Medical Faculty, Lampung University,

Jalan Prof. Soemantri Brojonegoro No,1, Bandar Lampung, Indonesia

Keywords: Factors, Malnutrition, Pregnancy, Principal Component Analysis.

Abstract: Malnutrition during pregnancy in Indonesia is quite high, and has the impact of increasing complications

during pregnancy and childbirth as well as in babies who are born. This research aims to analyze factors

associated with malnutrition in pregnancy using principal component analysis. This study is an observational

analytic using a case-control design. The research started from September to December 2021. The samples

for this study were 190 pregnant women in Bandar Lampung City and were taken using a multistage random

sampling technique that met the inclusion and exclusion criteria. The independent variables are nutrition

intake (energy, protein, carbohydrates, fat, iron), anemia, iron status, protein status, weight gain during

pregnancy, age, education, knowledge, occupation, income, parity, food taboo, and BMI before pregnant, and

the dependent variable is malnutrition during pregnancy. Nutrition intake data was assessed based on the

SQFFQ questionnaire. Data on anemia, iron status, and protein status were assessed based on blood tests.

Prepregnancy BMI and weight gain data were measured using anthropometry. Other data with questionnaires.

Data were analyzed using principal component analysis. The results of the study showed that based on

principal component analysis, 3 categories of factors were found that played a role in the incidence of

malnutrition in pregnancy, that were factor 1 (energy intake, protein intake, carbohydrate intake, fat and iron

intake) which was named nutrition intake; factor 2 (BMI before pregnancy and weight gain during pregnancy)

was named prepregnancy nutritional status and weight gain; and factor 3 (iron status, parity, and food taboo)

was named iron reserves and cultural factors.

1 INTRODUCTION

One of the key issues is malnutrition, which impairs

a person's physical function to the point where it is

unable to support necessary bodily functions like

growth, physical labor, and illness resistance or

recovery. Low birth weight, intrauterine growth

retardation (IUGR), and mother and child mortality

and morbidity are frequently caused by poor nutrition

during pregnancy, when combined with infections

(Serbesa et al., 2019).

Malnutrition is known to increase the risk of poor

pregnancy outcomes, including obstructed labor,

premature or low-birth-weight (LBW) babies and

postpartum hemorrhage (Gebre et al., 2018). Severe

anemia during pregnancy is associated with increased

maternal mortality. Besides, malnutrition among

https://orcid.org/0000-0003-0233-6635

https://orcid.org/0000-0003-1460-6428

mothers has an intergenerational effect, with

repeating cycles of malnutrition and poverty in the

long run (Saha et al., 2022).

Inadequate maternal nutrition has been linked to

an increased risk of complications like intrauterine

growth restriction, low birth weight, premature,

prenatal and child mortality, and morbidity (Marshall

et al., 2022). Inadequate nutrient intake, on the other

hand, has been shown to have pathophysiologic

effects that manifest as growth and development

defects in children and adult chronic disease in adults

over time (Norman et al., 2021).

Malnutrition remains one of the world’s highest

priority health issues, not only because its effects are

so widespread and long lasting but also because it can

be eradicated best at the preventive stage (Serbesa et

al., 2019). Maternal malnutrition is influenced not

Angraini, D. I. and Zuraida, R.

Factors Associated with Malnutrition in Pregnancy: A Principal Component Analysis.

DOI: 10.5220/0013668000003873

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Medical Science and Health (ICOMESH 2023), pages 195-202

ISBN: 978-989-758-740-5

195

only by lack of adequate nutrition but also influenced

by social and psychological factors, nutritional

knowledge of mothers, and biological changes that

influence perceptions of eating patterns during

pregnancies (Dukhi, 2020).

The factors that determine the CED status of a

woman of childbearing age, whether pregnant or not

pregnant, consist of direct, indirect, basic and main

problems. Direct factors include food intake and

illnesses suffered (infectious diseases, anemia,

protein deficiency). Indirect factors include food

availability, environment (family, environmental

cleanliness, culture), history of illness/health, health

services, obstetric status/parity, mother's education

and knowledge (UNICEF, 2015; Ministry of Health

of the Republic of Indonesia, 2015).

Of the many factors that play a role or are

associated with the incidence of malnutrition in

pregnancy, an analysis will be carried out using

principal component analysis to reduce a large

number of variables into a small number of factors.

The purpose of principal component analysis is to

explain variations in a set of observed variables on the

basis of several dimensions, from variables that

change a lot to variables that are few.

2 SUBJECT AND METHOD

This research is an observational analytic study with

a cross-sectional research design. The study was

conducted at the 12 Public Health Centers in Bandar

Lampung City, Indonesia, from September to

December 2021. The population in this study were

pregnant women in Lampung Province. Based on the

results of the sample calculation, the minimum

number of samples that must be met were 190

pregnant women in Bandar Lampung City. The

sample size calculation uses the sample size formula

for unpaired categorical comparative analytics with a

95% confidence value, the power of the test is 80%.

Sampling was done by the multistage random

sampling method.

The inclusion criteria were pregnant women and

willing to take part in the research. The exclusion

criteria were having a history of malignant disease,

suffering from or having a history of diabetes

mellitus, and suffering from or having a history of

infectious disease.

The independent variables are nutrition intake

(energy, protein, carbohydrates, fat, iron), anemia,

iron status, protein status, weight gain during

pregnancy, age, education, knowledge, occupation,

income, parity, food taboo, and BMI before pregnant,

and the dependent variable is malnutrition during

pregnancy. Nutrition intake data was assessed based

on the Semiquantitatve Food Questionaire (SQFFQ).

Data on anemia, iron status, and protein status were

assessed based on blood tests. Prepregnancy BMI and

weight gain data were measured using

anthropometry. Other data with questionnaires. Data

collection was carried out by researchers with the

help of 4 enumerators who had been given previous

guidance and training. The data was analyzed with a

significant degree of 95% (p<0.05) principal

component analysis. This research was carried out

after obtaining a research ethical clearance letter from

the Ethics Committee of the Faculty of Medicine, the

University of Lampung with number

3380/UN26.18/PP.05.02.00/2021.

3 RESULTS

The role of the variables that were energy intake,

protein intake, carbohydrate intake, fat intake, iron

intake, anemia, iron status, protein status, weight gain

during pregnancy, age, education, knowledge,

occupation, income, parity, food taboo, and BMI

before Pregnancy is tested using principal component

analysis (PCA) so that factor names can be obtained

for all these variables. The results of the first/initial

step to carry out factor analysis using the PCA

method are presented in table 1. Based on the results

of the initial PCA analysis test, it was found that the

Kaiser-Meyer-Olkin (KMO) value was 0.692 (greater

than 0.5) so that the factor analysis technique could

be continued and the Bartlett's test of sphericity value

was 0.000 (p<0.05), so the factor analysis in this

study could be continued because it met the

requirements First.

The Measurement System Analysis (MSA) value

for each variable can be seen based on the anti-image

matrix value. Based on the results of PCA analysis,

not all variables have an MSA value >0.5. Variables

that have an MSA value >0.5 are energy intake,

protein intake, carbohydrate intake, fat intake, iron

intake, anemia, iron status, weight gain during

pregnancy, education, income, parity, food taboo, and

BMI before pregnancy. Variables that have an MSA

value <0.5 are protein status, age, knowledge, and

occupation. Because not all variables have an MSA

value >0.5, the second requirement for PCA analysis

is not fulfilled so the analysis cannot continue.

Therefore, the factor analysis process was carried out

using the PCA method again only for variables that

had an MCA value >0.5.

ICOMESH 2023 - INTERNATIONAL CONFERENCE ON MEDICAL SCIENCE AND HEALTH

196

Table 1: Results of Initial Factor Analysis Using the

Principal Component Analysis (PCA) Method

Variables KMO

Bartlett

didn't tes

Antiimage

matrix

0.692 0,000

Energy intake

Protein intake

Carbohydrate intake

Fat intake

Iron intake

Anemia

Serum ferritin status

Serum albumin status

Weight gain

Age

Education

Knowledge

Occupation

Income

Parity

Food taboo

BMI before pregnancy

0.725

0.864

0.723

0.769

0.870

0,510

0,522

0,484*

0,561

0,456*

0,509

0,497*

0,487*

0,519

0,725

0,612

0,568

Analysis using the PCA method was carried out

a second time by including the variables energy

intake, protein intake, carbohydrate intake, fat intake,

iron intake, anemia, iron status, weight gain during

pregnancy, education, income, parity, occupation,

and BMI before pregnancy. Based on the results of

the initial PCA analysis test, it was found that the

KMO value was 0.733 (greater than 0.5) so that the

factor analysis technique could be continued and the

Bartlett's test of sphericity value was <0.001

(p<0.05), so the factor analysis in this study could be

continued because it met the requirements First.

The MSA value for each variable can be seen

based on the anti-image matrix value. Based on the

results of PCA analysis, not all variables have an

MSA value >0.5. Variables that have an MSA value

>0.5 are energy intake, protein intake, carbohydrate

intake, fat intake, iron intake, iron status, weight gain

during pregnancy, parity, food taboo, and BMI before

pregnancy. Variables that have an MSA value <0.5

are anemia, education and income. Because not all

variables have an MSA value >0.5, the second

requirement for PCA analysis is not fulfilled so the

analysis cannot continue. Therefore, the factor

analysis process was carried out using the PCA

method again only for variables that had an MCA

value >0.5.

Analysis using the PCA method was carried out

three times by including the variables energy intake,

protein intake, carbohydrate intake, fat intake, iron

intake, iron status, weight gain during pregnancy,

parity, food taboo, and BMI before pregnancy. The

results of the two factor analyses using the principal

component analysis (PCA) method are presented in

table 2. Based on the results of the initial PCA

analysis test, it was found that the KMO value was

0.756 (greater than 0.5) so that the factor analysis

technique could be continued and the Bartlett's test of

sphericity value was <0.001 (p<0.05), so the factor

analysis in this study could be continued because it

met the requirements First.

The MSA value for each variable can be seen

based on the anti-image matrix value. Based on the

results of the PCA analysis, all variables have an

MSA value >0.5 so that the second requirement for

this PCA analysis is met and the analysis can

continue, and this is the result of the final factor

analysis.

The communalities column shows how much the

factors formed can explain the variance of a variable.

The communalities value for all these variables is the

same, namely 1,000, which means that each variable

can explain 100% of the variance of the factors

formed, thus indicating the close relationship between

the variables in question and the factors formed

(Delsen, Wattimena & Saputri, 2017).

Table 2: Final Factor Analysis Results Using the Principal Component Analysis (PCA) Method

Variables KMO

Bartlett

didn't test

Anti-image

matrix

Commu-

nalities

Initia

envalue

Eigenvalues

0.756 0,000

Energy intake

Protein intake

Carbo intake

Fat intake

Iron intake

Ferritin status

Weaight gain

Parity

Food taboo

BMI Prepregnancy

0.746 0.898

0.757 0.815

0.882 0.521

0.553 0.754

0.652 0.561

1,000

3,278

1,635

1,095

0.946

0.847

0.597

0.534

0.480

0.365

0.224

3,278

1,635

1,095

Factors Associated with Malnutrition in Pregnancy: A Principal Component Analysis

197

To determine the number of factors formed, it can

be based on the eigenvalue. If there is a total

eigenvalue whose value is less than 1, the factor is

declared unable to explain the variable well, so it is

not included in the formation of the variable (Umar,

2009). The results of the analysis show that the eigen

value shows that the number of variants obtained in

the output results is all three (3) variants, meaning

that there are three (3) groups of factors that may be

formed. These three variables can explain the

variance of the eight items amounting to 60.08%.

This figure is quite large because it is proven to

explain more than 50% of the variance of the variable.

The number of factors can also be determined

from the scree plot. A scree plot is like a broken line.

The point where the scree starts to occur shows the

number of factors, precisely when the scree starts to

flatten, shown by the initial eigen value which is > 1.

The results of the analysis in this study show that the

initial eigen value > 1 is component number 1.

Figure 1. Scree Plot

The results of the analysis then show a

component matrix of factors that associated with

malnutrition in pregnancy. The component matrix

and rotation model are presented in table 3. The

matrix component shows the correlation value

between a variable and the factors formed. The

energy intake variable has the highest variable

correlation on factor 1, that was 0.872 compared to

the correlation on factors 2 and 3, so that the energy

intake variable is on factor 1. The protein intake

variable has the highest variable correlation on factor

1, namely 0.764 compared to the correlation on

factors 2 and 3 so that the protein intake variable is at

factor 1.

The carbohydrate intake variable has the highest

variable correlation on factor 1, that was 0.760

compared to the correlation on factors 2 and 3, so that

the carbohydrate intake variable is on factor 1. The fat

intake variable has the highest variable correlation on

factor 1, that was 0.753 compared to the correlation

on factors 2 and 3 so that the fat intake variable is in

factor 1. The iron intake variable has the highest

variable correlation in factor 1, that was 0.710

compared to the correlation in factors 2 and 3 so that

the iron intake variable is in factor 1.

The iron status variable has the highest

variable correlation in factor 3, that was 0.778

compared to the correlation in factors 1 and 2, so that

the iron status variable is in factor 3. The weight gain

variable during pregnancy has the highest variable

correlation in factor 2, that was 0.821 compared to the

correlation in factors 1 and 3 so that the weight gain

variable during pregnancy is in factor 2. The

abstinence from eating variable has the highest

variable correlation in factor 3, that was 0.650

compared to the correlation in factors 1 and 2 so that

the abstinence from eating variable is in factor 3.

Table 3. Matrix Components and Rotation Model Results of Application Analysis of Factors Associated with Malnutrition in

Pregnancy

Variables Component Rotation Model

1 2 3 1 2 3

Energy intake 0.872* 0.166 0.054 0.732 0.347 0.286

Protein intake 0.764* 0.099 0.081 0.912 0.223 0.215

Carbohydrate- intake 0.760* 0.207 0.156 0.923 0.154 0.205

Fat intake 0.753* 0.125 0.061 0.921 0.165 0.207

Iron intake 0.710* 0.184 0.013 0.928 0.197 0.210

Ferritin status 0.105 0.054 0.77* 0,000 0.014 0.997

Weight gain 0.272 0.82* 0.053 0.011 0.940 0.042

Parity 0.253 0.158 0.56* 0.025 0.064 0.989

Food taboo 0.225 0.161 0.66* 0.084 0.064 0.992

BMI Pre-pregnancy 0.296 0.80* 0.043 0.024 0.938 0.025

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The parity variable has the highest variable

correlation on factor 3, that was 0.564 compared to

the correlation on factors 1 and 2, so the parity

variable is on factor 3. The BMI variable before

pregnancy has the highest variable correlation on

factor 2, that was 0.808 compared to the correlation

on factors 1 and 3 so that the BMI variable before

pregnancy is in factor 2. Through the rotation model,

3 rotations of matrix components are produced,

according to the number of factors obtained. From the

results of the analysis it was found that after rotation

there were three variables that were highly correlated

(cut off point = 0.55), that were:

1) Factor 1: energy intake, protein intake,

carbohydrate intake, fat intake and iron intake.

2) Factor 2: weight gain during pregnancy, and BMI

before pregnancy

3) Factor 3: iron status, parity, and food abstinence

4 DISCUSSION

The results of the study show that the factors

associated with the incidence of malnutrition in

pregnancy consist of 3 factors, that were food intake

factors (energy intake, protein intake, carbohydrate

intake, fat intake and iron intake), nutritional status

factors, that were pre-pregnancy BMI and weight

gain during pregnancy, and iron status and cultural

factors (parity and food taboo).

The increase in energy in pregnant women is used

for growth and development of the fetus, placenta and

health maintenance. Pregnant women who consume

food with a number of calories below the

recommended adequacy for a long time will risk

malnutrition which can cause the fetus to grow

imperfectly. Energy should be balanced, otherwise it

will have detrimental effects on the body (Lowensohn

et al., 2016).

When the amount of energy consumed through

food is less than the amount of energy used, there is

an energy deficit. Body weight is therefore lower than

the optimal body weight. It will impede growth in

infants and young children if it happens. In the

meanwhile, it will cause tissue damage and weight

loss if it affects adults (Mahdi et al., 2023).

Insufficient nutritional intake both before and

during pregnancy can cause pregnant women to

become malnourished. The needs of pregnant women

are greater than the needs of non-pregnant women.

Pregnant women's energy needs need to be increased

according to gestational age (Jouanne et al., 2021).

The energy needs of pregnant women in the 1st

trimester are increased by 180 kcal/day, in the 2nd

trimester an additional 300 kcal/day and in the 3rd

trimester an additional 300 kcal/day.

This increased energy is used for growth and

development of the fetus, placenta and health

maintenance. Pregnant women who consume food

with a number of calories below the recommended

intake for many years will risk malnutrition which

can cause the fetus to grow imperfectly (Lowensohn

et al., 2016).

Protein serves as an enzyme and hormone

regulator as well as a building block for the body's

structural proteins, such as collagen and elastin.

Moreover, proteins serve as immune response

mediators and transporters of certain proteins.

Pregnant women's protein requirements must be met

because the fetus's capacity to grow normally

depends on the mother's supply of sufficient protein.

Protein is the basic building material needed for the

formation of enzymes, antibodies, muscles and

collagen. Collagen is used as a framework for skin,

bones, blood vessels and other body tissues (Elango

& Ball, 2016).

During pregnancy the mother consumes enough

protein to meet the increasing needs of herself and the

developing fetus. Protein is a source of energy after

glycogen, being a catalyst for biochemical reactions

in the body, forming the structure of cells and tissues.

Therefore, individuals must get sufficient protein

intake because protein deficiency will have a negative

impact on an individual, especially preconception

women, pregnant women and adolescent girls

(Elango & Ball, 2016).

Carbohydrates are the main source of energy for

humans. Every 1 gram of carbohydrate consumed

produces 4 kcal of energy and the results of the

carbohydrate oxidation process will then be used by

the body to carry out various functions such as

breathing, heart contractions, and to carry out various

physical activities (Morris & Mohiuddin, 2023).

Carbohydrates perform various important

functions for the body, namely as a source of energy,

helping fat metabolism, preventing excessive

breakdown of body protein. Pregnant women who

have a low level of carbohydrate consumption should

consume more bread, rice, cereal, etc., including fruit

and vegetables that contain carbohydrates (Prasetyo,

2017).

Carbohydrate consumption as the largest energy

contributor must be adjusted to the body's needs.

Apart from excessive intake which will cause excess

weight, if the intake is insufficient then malnutrition

will occur. Lack of energy in the body will cause

changes in carbohydrates, proteins or fats to become

energy sources, so that the main function of these

Factors Associated with Malnutrition in Pregnancy: A Principal Component Analysis

199

three nutrients will decrease. If these changes last for

a long time, there will be changes in body weight and

damage to body tissue. Energy in the human body can

arise due to the burning of carbohydrates, proteins

and fats, so humans need sufficient food substances

to meet their energy requirements (Clemente-Suarez

et al., 2022).

Fat functions as a solvent for vitamins A, D, E,

and K. Fat also functions as an energy reserve for the

mother when she gives birth. The need for fat in

pregnant women is very important because it is used

as an energy reserve during and after the birth process

and breastfeeding. Therefore, pregnant women must

consume fat in balanced amounts, neither more nor

less (Duttaroy & Basak, 2021).

Fat functions as a source of calories in preparation

for childbirth and to metabolize vitamins A, D, E and

K. Pregnant women who do not consume enough fat

are feared that they will lack energy during pregnancy

and childbirth which will affect the baby to be born.

Fat also functions in the development of the brain and

nerves, so that if there is a deficiency it can result in

imperfect growth of the fetus's nerves, whereas if the

mother experiences an excess of fat consumption, the

baby and the fetus will accumulate energy. Food

sources that can produce fat are oil, margarine and

butter (Marshall et al., 2022).

Iron requirements increase during pregnancy for

maternal and fetal blood formation. In addition, the

mother's iron stores are needed for 4-6 months after

birth to meet the baby's needs, because the baby's

main food in the form of breast milk contains little

iron. Additionally, mothers lose a lot of blood during

delivery. Sources of iron are animal foods such as

liver, meat, chicken, fish and eggs. Plant foods such

as cereals, nuts and their processed products, as well

as green vegetables but have lower biological

availability (Georgieff, 2020).

The role of iron is very essential in pregnancy, in

fact various health programs are directed at meeting

iron needs during pregnancy, especially through

supplementation and fortification programs. Apart

from that, information exposure through counseling

and various communication media is continuously

carried out to provide a comprehensive understanding

regarding the importance of meeting iron needs in

pregnancy. This causes pregnant women, especially

those with higher education, to be able to accept the

information and health programs provided in an effort

to fulfill their iron needs. Pregnant women with

higher education also show better iron consumption

(Brannon & Taylor, 2017).

The mother's pre-pregnancy BMI is considered to

indicate the quality of the mother's nutrition during

the pre-pregnancy period as well as showing the

availability of nutrients in the mother's body tissues

before pregnancy, which will have an impact on the

mother's health and the growth of the fetus while in

the womb. The risk of giving birth to a small baby for

gestational age decreases along with an increase in

BMI before entering pregnancy. BMI before

pregnancy is the most appropriate research to predict

the quality of the baby born (Bonakdar et al., 2019).

Maternal nutritional adequacy during pregnancy

can also be evaluated through weight gain during

pregnancy. Weight gain during pregnancy is the

difference between initial body weight and final body

weight, where final body weight is the body weight

several weeks before giving birth. Another definition,

weight gain during pregnancy is the difference

between the weight at the end of pregnancy and the

weight at the beginning of pregnancy. Final weight is

the weight at birth. Meanwhile, initial weight is the

weight at the initial pregnancy examination. Under

normal circumstances, the mother's weight gain from

the beginning of pregnancy is calculated from the first

trimester to the third trimester (Mamidi et al., 2017).

Maternal weight gain during pregnancy is directly

correlated with the incidence of malnutrition, because

both reflect the adequacy of pregnant women's food

intake during pregnancy. Insufficient pregnancy

weight gain will cause the risk of stunted fetal growth

(IUGR, LBW, PBLR) and prematurity (Abubakari et

al., 2023).

Iron status in the body can be assessed based on

the ferritin indicator, which shows iron stores in the

first stage of iron depletion. Ferritin is formed when

apoferritin binds to iron. Ferritin, which is the main

storage form of iron in the body, is mainly found in

the liver, pancreas and spinal cord. In healthy

humans, approximately 30% of iron is found in

storage form, mainly as ferritin and partly as

hemosiderin. If iron reserves are depleted, ferritin in

the tissue decreases. Measurement of ferritin

concentration is the most sensitive index for detecting

iron deficiency before morphological changes are

seen in red blood cells, and before anemia occurs

(Martinez et al., 2021).

Parity is one of the factors causing malnutrition in

pregnant women. Parity is the number of children

born to a mother. Parity is divided into nullipara,

primipara, multipara and grandemultipara. Nullipara

is a woman who has never given birth to a fetus,

primipara is a woman who has given birth to a fetus

once, multipara is a woman who has given birth to a

fetus >1x, and grandemultipara is a woman who has

given birth to a fetus >5x (Cunningham et al., 2018).

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Parity is a risk factor for malnutrition in

pregnancy. Pregnant women who have a parity of

more than 4 people are at greater risk of malnutrition

compared to mothers who have a parity of less than 4

people. Pregnancies that are too frequent (high

parity), that was a mother who already has three

children and another pregnancy occurs, her health

condition will begin to decline (Karemoi et al., 2020).

Food taboos are foodstuffs or dishes that cannot

be eaten by individuals in society for cultural reasons.

Some dietary taboo patterns are only adhered to by a

certain group of people or by a larger portion of the

population. Other patterns only apply to groups

within a particular population and at a particular time.

If the taboo pattern applies to the entire population

and throughout life, nutritional deficiencies are less

likely to develop as if the taboo only applies to a

certain group of people during one stage of the cycle

(Chakona & Shackleton, 2019).

Some dietary restrictions are only adhered to by a

certain group of people or by a larger part of the

population. Other patterns only apply to certain

groups within a population and at certain times. If the

pattern of abstinence applies to the entire population

and throughout life, nutritional deficiencies are less

likely to develop as if the taboo only applies to a

certain group of people during one stage of the cycle

(Ojo et al., 2023).

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